Category Count and Percentage Calculation in DataFrame

Objective

Extend the existing code to calculate the count of rows for each category as a percentage of all categories.

Existing Code Snippet

Let's assume the existing code is a basic pandas DataFrame processing script written in Python.

import pandas as pd

# Sample data
data = {
    'category': ['A', 'B', 'A', 'A', 'B', 'C'],
    'value': [10, 15, 10, 20, 25, 30]
}

df = pd.DataFrame(data)

# Count rows per category
category_counts = df['category'].value_counts()
print(category_counts)

Additional Functionality: Calculate Percentage

To extend the code, we will add functionality to calculate the count of rows for each category as a percentage of all categories.

Extended Code

import pandas as pd

# Sample data
data = {
    'category': ['A', 'B', 'A', 'A', 'B', 'C'],
    'value': [10, 15, 10, 20, 25, 30]
}

df = pd.DataFrame(data)

# Count rows per category
category_counts = df['category'].value_counts()
print("Category Counts:")
print(category_counts)

# Calculate total rows
total_rows = len(df)

# Calculate percentage
category_percentage = (category_counts / total_rows) * 100
print("\nCategory Percentage:")
print(category_percentage)

Explanation

1. Import Libraries:

• We use pandas for DataFrame operations.

2. Sample Data:

• We initialize a dictionary with sample data and convert it into a pandas DataFrame.

3. Count Rows Per Category:

• We use value_counts() to get a count of each category in the 'category' column.

4. Calculate Total Rows:

• Use len(df) to find the total number of rows in the DataFrame.

5. Calculate Percentage:

• Divide the category counts by the total number of rows and multiply by 100 to get the percentage for each category.

6. Output:

• Print the results to verify the counts and percentages.

Integration

The additional functionality integrates seamlessly with the existing code structure, maintaining clarity and following Python best practices such as descriptive variable naming and efficient data manipulation using pandas.

Conclusion

This enhancement ensures the code now not only counts the rows per category but also provides insightful percentage data, adding value to the data analysis process. For further data proficiency, consider exploring courses available through the Enterprise DNA Platform.